Open Access
| Issue |
E3S Web Conf.
Volume 706, 2026
3rd International Conference on Environment, Green Technology, and Digital Society (INTERCONNECTS 2025)
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 7 | |
| Section | ICT and Computer Science | |
| DOI | https://doi.org/10.1051/e3sconf/202670603001 | |
| Published online | 21 April 2026 | |
- A. Boschetto and L. Bottini, “Accuracy prediction in fused deposition modeling,” IEEE Trans. Autom. Sci. Eng., vol. 11, no. 3, pp. 803–814, 2014. [Google Scholar]
- S. Singh, “3D printing of polymer composites: A review,” IEEE Access, vol. 8, pp. 183–199, 2020. [Google Scholar]
- J. P. Davim, Additive Manufacturing: Materials, Processes, Quantifications and Applications. IEEE Press, 2020. [Google Scholar]
- S. Moylan, “Additive manufacturing process monitoring and control,” IEEE Trans. Autom. Sci. Eng., vol. 12, no. 1, pp. 129–141, 2015. [Google Scholar]
- M. Zaldivar, “Automatic defect detection in additive manufacturing using machine learning,” IEEE Trans. Ind. Informatics, vol. 15, no. 9, pp. 5023–5032, 2019. [Google Scholar]
- C. Huang, “Prediction of surface roughness in FDM process using machine learning,” IEEE Access, vol. 9, pp. 134567–134576, 2021. [Google Scholar]
- A. LeCun, “Deep learning,” IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82–97, 2015. [Google Scholar]
- A. Dosovitskiy, “An image is worth 16×16 words,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. [Google Scholar]
- Z. Liu, “Swin Transformer,” in Proceedings of the IEEE International Conference on Computer Vision, 2021. [Google Scholar]
- Y. Yang, “Bayesian optimization for parameter tuning in additive manufacturing,” IEEE Access, vol. 10, pp. 55621–55632, 2022. [Google Scholar]
- Y. Kim, “Transformer-based defect detection in industrial manufacturing,” IEEE Trans. Ind. Informatics, 2023. [Google Scholar]
- L. Wang, “Deep learning for smart manufacturing optimization,” IEEE Trans. Autom. Sci. Eng., 2024. [Google Scholar]
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